Coronary artery disease (CAD) is one of the most common causes of death in the United States, accounting for nearly 500,000 deaths each year. Studies estimate that 50% of men and 33% of women under the age of 40 will develop some form of CAD sometime during their lifetime. Sudden cardiac death has steadily accounted for approximately 50% of all heart-related, out-of-hospital deaths and improved clinical procedures almost entirely ignore this group. The fact that patients generally fail to recognize their symptoms and seek prompt medical attention contributes to these tragic statistics. The principal manifestations of CAD are coronary artherosclerosis (hardening of coronary arteries) or stenosis (narrowing of arteries), both of which ultimately force a reduction of the coronary circulation (myocardial ischemia, infarction, or other kind of cardiac arrhythmia). However, the early stages of CAD are usually non-symptomatic and invisible with current clinical cardiac signal analysis strategies.
Early arrhythmia recognition is critical for rhythm management of cardiac disorders. Currently, signal waveform and time domain parameter analysis of cardiac cycle depolarization and repolarization, such as P wave, QRS complex, ST segment, T wave, are used for cardiac arrhythmia monitoring and identification. However, such traditional clinical methodologies are sometimes subjective and time-consuming, requiring the user to possess expertise and clinical experience to achieve accurate interpretation and proper cardiac rhythm management.
Traditional medical methods usually focus on time domain analysis (e.g., amplitude, latency, etc.) or frequency domain analysis (e.g., power, spectrum, etc.), which may not accurately capture minute signal changes in the partial signal portion (e.g., P wave, QRS complex, ST segment, etc.) of cardiac activities. Such signal changes are usually invisible in signal wave morphology display or require extensive clinical expertise to achieve accurate diagnosis. Consequently, it may result in high failure rate of arrhythmia diagnosis and high number of false alarms. For example, a false negative (FN) results when the screening test wrongly makes the decision that a subject does not have disease X (e.g., myocardial ischemia) when he or she does in fact have the disease. These concerns raise a need for a new approach to precisely extract arrhythmia pathology information with high reliability and sensitivity from ongoing cardiac signals, which can diagnose partial signal portions of heart tissues.
Further, traditional methods based on voltage amplitude changes and variation analysis may not be sufficient for cardiac function evaluation and pathology diagnosis, especially since there is no quantitative link between the myocardial ischemia event/status and the amplitude and variation index. Known clinical diagnosis of myocardial ischemia (MI) and detection of infarction are based on the gold clinical standard based on ST segment voltage deviation (e.g., 0.1 mV elevation for myocardial ischemia detection). However, there are at least two shortcomings with this gold standard: (a) this standard only works for surface ECG signals, but not for intra-cardiac electrogram (ICEG) signals; (b) ST segment deviation (voltage) cannot be utilized as a quantitative method for myocardial ischemia severity diagnosis and characterization.
Current methods may not be able to qualitatively and quantitatively capture or characterize minute signal changes and predict the pathological trend, especially in the early stage of tissue malfunctioning and acute myocardial ischemia. Known methods may not efficiently analyze and predict the real-time growing trend of cardiac arrhythmias, such as the pathology trend from low risk to medium risk, and then to high risk (i.e., severe and fatal) rhythm, especially for life threatening arrhythmia, such as ventricular tachycardia (VT).